Sunflower Optimization Algorithm

Mohammad Ehteram (), Akram Seifi () and Fatemeh Barzegari Banadkooki ()
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Mohammad Ehteram: Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering
Akram Seifi: Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture
Fatemeh Barzegari Banadkooki: Payame Noor University, Agricultural Department

Chapter Chapter 4 in Application of Machine Learning Models in Agricultural and Meteorological Sciences, 2023, pp 43-47 from Springer

Abstract: Abstract This chapter explains the mathematical model and structure of sunflower optimization (SFO). The algorithm acts based on the life of the SFO. When the distance of a flower from the sun increases, the radiation intensity decreases. The sunflower seeks the best orientation toward the sun. The SFO has a high ability to solve optimization problems. The SFO can be easily implemented for solving complex problems. The SFO outperformed the other optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), and other algorithms. The SFO can be used for solving complex problems in different fields. It also can be used for training soft computing models. The SFO can be coupled with other optimization algorithms for solving complex problems.

Keywords: Sunflower optimization algorithm; Convergence velocity; Training soft computing models; Optimization algorithms (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-9733-4_4

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DOI: 10.1007/978-981-19-9733-4_4

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